Abstract

Background Individuals in lower socio socio-economic groups have an
increased prevalence of common mental disorders.

Aims To investigate the longitudinal association between
socio-economic position and common mental disorders in a general population
sample in the UK.

Method Participants (n=2406) were assessed at two time
points 18 months apart with the Revised Clinical Interview Schedule. The
sample was stratified into two cohorts according to mental health status at
baseline.

Results None of the socio-economic indicators studied was
significantly associated with an episode of common mental disorder at
follow-up after adjusting for baseline psychiatric morbidity. The analysis of
separate diagnostic categories showed that subjective financial difficulties
at baseline were independently associated with depression at follow-up in both
cohorts.

Conclusions These findings support the view that apart from
objective measures of socio-economic position, more subjective measures might
be equally important from an aetiological or clinical perspective.

Previous cross-sectional studies have shown that people in lower
socio-economic groups have an increased prevalence of common mental disorders
(Holzer et al, 1986;
Bijl et al, 1998;
Davey Smith et al,
1998; Lewis et al,
1998; Muntaner et al,
1998; Weich & Lewis,
1998a). Cross-sectional studies cannot distinguish
whether low socio-economic position is associated with the development of new
episodes of common mental disorders, with increased duration of episodes or
both (Muntaner et al,
2004). Psychiatric disorders often show a chronic course
(Sargeant et al,
1990) and it is likely that patients in the lower socio-economic
groups might have a worse prognosis rather than an increased risk of a new
episode of disorder (Lewis et al,
1998). Previous longitudinal studies have generally supported this
observation (Weich & Lewis,
1998b) and a recent meta-analysis found stronger evidence
in favour of an association with increased duration
(Lorant et al, 2003).
However, other studies have found that low socio-economic position may be a
risk factor for the development of a new episode
(Kaplan et al, 1987;
Bruce et al, 1991).
These conflicting results may be explained by the different samples and method
used, and the inability to adjust for a number of potential confounders. In
particular, it is not clear whether all previous studies adjusted for baseline
psychiatric symptoms, even though this variable shows a strong association
with persistence of disorder (Sargeant
et al, 1990; Spijker
et al, 2001). Similarly, subthreshold symptoms may
confound the association between low socio-economic position and development
of a new episode of disorder. Clarifying whether low socio-economic position
is associated with increased risk of a new episode of common mental disorder
or with worse prognosis is critical from both an aetiological point of view
and a public health perspective. The aim of our study was to investigate this
issue in a longitudinal, general-population study in the UK. Based on the
previous findings we predicted that participants of lower socio-economic
position would be more likely to report an episode of a common mental disorder
at follow-up and that this association would be stronger in those who were
categorised as cases at baseline compared with non-cases.

METHOD

Data-set

The longitudinal study reported here was conducted in the UK by the Office
for National Statistics (ONS). The 2000 Psychiatric Morbidity Survey aimed to
estimate the prevalence of common mental disorders and the use of services of
adults, aged 16-74 years, living in private households in Great Britain
(Singleton et al,
2001). The sample was drawn from the small-user Postcode Address
File using a two-stage approach. Initially, postcode sectors were stratified
on the basis of socio-socio-economic status within region and 438 sectors
selected with a probability proportional to size. Then, within each selected
sector, 36 addresses were randomly selected for inclusion in the survey.
Interviewers visited each address to identify private households with at least
one person aged 16-74 years and then one person per household was randomly
selected for interview. The main fieldwork took place between March and
September 2000 and interviews were available for 8580 individuals (67%
response rate).

Eighteen months later 3536 of the original respondents (all of those with a
definite or sub-threshold psychiatric disorder and a 20% random sample of
those without such disorder) were contacted for a follow-up interview and 2413
were successfully reinterviewed (68% response rate). Non-participants included
620 people who could not be traced or contacted (18%) and 503 who refused
(14%). Non-participants were slightly more likely to be younger and of lower
socio-economic status (Singleton &
Lewis, 2003). Owing to some incomplete interviews, the present
study reports findings from the 2406 individuals for whom full data were
obtained on both occasions. Ethical approval for the survey work was obtained
from the Multi-Centre Research Ethics Committees in England. Further details
of the survey method are available elsewhere
(Singleton & Lewis,
2003).

Measurement of psychiatric morbidity

Revised Clinical Interview Schedule

Psychiatric morbidity in the week preceding interview was assessed using
the Revised Clinical Interview Schedule (CIS-R;
Lewis et al, 1992), a
structured interview designed to be used by trained lay personnel. It can
provide data on the prevalence of 14 symptoms, six ICD-10 disorders
(depressive episode, phobias, generalised anxiety disorder, panic disorder,
obsessive-compulsive disorder, mixed anxiety and depression disorder;
World Health Organization,
1992) and the distribution of total CIS-R scores, which gives an
indication of severity of symptoms in a dimensional way. The CIS-R was
selected because it had been used in the first nationally representative
general population survey of psychiatric morbidity in the UK made in 1993 by
the Office for National Statistics
(Jenkins et al,
1997). It has been used in several other surveys around the world
and is comparable to other structured interviews used in epidemiological
surveys, such as the Composite International Diagnostic Interview (CIDI;
Robins et al, 1988).
The reliability of the CIS-R has been studied in primary care samples and the
kappa coefficient of reliability was reported to be 0.72 (95% CI 0.65-0.79)
(Lewis et al,
1992).

Diagnoses

Diagnoses of ICD-10 disorders were derived by applying specific algorithms
that had been developed in a previous general population survey
(Jenkins et al, 1997)
according to the ICD-10 diagnostic criteria for research
(World Health Organization,
1992). All diagnoses refer to the 7 days before the interview. It
should be noted that the diagnosis of `mixed anxiety and depression' (ICD-10
code F41.2) refers to a clinically important disorder (not sub-threshold
disorder) that does not meet criteria for another anxiety or depressive
disorder. The ICD-10 does not include specific diagnostic criteria for this
condition, but suggests that researchers should use their own depending upon
the setting and the purpose of their study. For this reason we defined as
cases of mixed anxiety and depression all those scoring 12 or more on the
CIS-R who did not meet criteria for any other anxiety or depressive disorder.
In order to avoid confusion with depressive disorder comorbid with anxiety
disorders, in the tables we refer to this condition as `non-specific
psychiatric morbidity'. The threshold of 12 or more was selected because it
has been found to represent the level of clinically important symptoms in the
UK (Lewis et al,
1992). We included these patients in our analysis because previous
psychiatric morbidity surveys had shown that mixed anxiety and depression was
the most common disorder in the UK general population, with a weekly
prevalence of approximately 9% (Singleton
et al, 2001). In addition, there is increasing research
interest in mild disorders, and a recent analysis of the National Comorbidity
Survey in the USA underlined the clinical importance of milder forms of mental
disorders (Kessler et al,
2003).

Definition of common mental disorders

In our main analysis we combined all cases of participants meeting criteria
for at least one definite ICD-10 disorder (of the six we assessed with the
CIS-R) into the category of `common mental disorders'. Individuals with
probable psychotic disorder at baseline were excluded from the analysis. Our
two main reasons for combining cases of psychiatric disorder in this common
category were, first, that previous research in the UK has shown that the
psychiatric problems seen in the community or primary care settings are better
described by one or two highly correlated dimensions of depression and anxiety
(Goldberg et al,
1987; Jacob et al,
1998), and second, that the power of the study was greatly
improved by this categorisation. However, to make our results more clinically
relevant, we also present analyses using separate ICD-10 diagnostic categories
as the dependent variable. Three analyses are presented in this respect:

cases of ICD-10 depressive episode;

cases of any anxiety disorder;

cases of non-specific psychiatric morbidity (i.e. all those meeting
criteria for mixed anxiety and depression as defined previously).

Measurement of socio-economic position

Social class

Occupational social class was defined according to the UK Registrar
General's classification and was based on the participant's current (or most
recent) occupation. Participants were classified into four categories:
professionals or intermediate occupations (I, II), non-manual skilled
occupations (III non-manual), manual skilled occupations (III manual) and
partly skilled occupations or unskilled occupations (IV, V). Social class was
not determined if the person had never worked or was a full-time student, or
if the occupation was inadequately described. For this reason, in the analysis
we added a fifth category corresponding to the missing values of this
variable.

Education

Educational qualifications (based on highest level attained) were
classified in four groups: university degree, teaching or nursing
qualifications, including honorary degrees; A-level qualifications; General
Certificate of Secondary Education (GCSE) or equivalent; no qualification.

Standard of living

Three variables were selected a priori to provide an assessment of
each participant's material standard of living: household gross income,
housing tenure and ability to pay for everyday needs. Weekly household gross
income was classified in three groups: £400 or more, less than £
400 but £200 or more and less than £200. Housing tenure
status was classified into three categories: owners, renters from the private
sector and renters from the public sector. Finally, participants were asked a
series of questions related to their ability to pay for their everyday needs
in the year preceding interview. These included questions on whether they were
seriously behind in paying bills, credit card debts, mortgage repayments and
loans; whether they had been subjected to disconnection by a utility company
or had used water, gas, electricity or the telephone less because they could
not afford it; and whether they had borrowed money from unofficial sources in
order to pay for their everyday needs. People who reported at least one
difficulty in these areas were classified as having experienced financial
difficulties.

To overcome the problem of colinearity between income and housing tenure
status, we derived a composite index of material standard of living by adding
these two indicators. We assigned numerical values to each group of income and
tenure status and added the two variables. The score on the composite index
ranged from 1 (wealthiest) to 6 (poorest) with a mean of 3.16 (s.d.=1.52) and
a median of 3. We analysed the question about financial difficulties
separately because of the more subjective nature of this measure and also
because previous research has shown that it may be different in nature from
the other two (Weich & Lewis,
1998b).

Other variables

We used information on the following variables: age (in 10-year intervals);
gender; marital status (in five categories: married, separated, single,
divorced, widowed); type of family unit (in five categories: couple without
children, couple with children, lone parent, one person only, adult with
parents); employment status (in three categories: working full-time or
part-time, unemployed, economically inactive).

Statistical analysis

We stratified the sample by case status at baseline and carried out
separate logistic regression analyses for the two cohorts of non-cases and
cases of common mental disorders. First, we examined the association between
socio-economic position and meeting criteria at the follow-up assessment (time
2) for an episode of common mental disorder in the cohort of non-cases
(n=1656). We then examined this association in the cohort of cases
(n=750). These two analyses were our best approximations of the terms
`onset' and `persistence' of common mental disorders as used by other papers
in the past (Weich & Lewis,
1998b; Lorant et
al, 2003). We consider the limitations of this approximation
in the Discussion section below.

We present three types of odds ratios: crude odds ratios, odds ratios
adjusted for all other socio-economic indicators and other socio-demographic
variables, and odds ratios further adjusted for baseline CIS-R score. The svy
commands in Stata version 7.0 for Windows were used for the analysis.
Probability weights were used to take account of the stratified sampling
procedure and non-response (Singleton & Lewis, 2003).

RESULTS

Baseline socio-demographic and socio-economic characteristics of the sample
are presented in Table 1 and
the sample's clinical characteristics are given in
Table 2. The most significant
predictor of a new episode of common mental disorder in participants free from
disease at baseline (`new onsets') was the score on the CIS-R
(Table 3). Social class was not
associated with an increased risk of a new episode even in the crude analysis.
Lower educational qualifications showed a trend for an increased risk of a new
episode but this was not statistically significant. Participants with a lower
material standard of living were more likely to develop a new episode of a
common mental disorder in the crude analysis but this was no longer
significant after adjustment for the other variables in the model. In
contrast, those reporting financial difficulties at baseline had an increased
risk of a new episode even after adjustment (model 1,
Table 3). However, in the final
model adjustment for baseline CIS-R score reduced the association, which
became non-significant (model 2, Table
3).

Odds ratios for an episode of common mental disorder at the 18-month
follow-up assessment in participants who were free from disease at baseline
(n=1656)1

Table 4 presents the results
for the cohort of cases of common mental disorder at baseline. In the crude
analysis, all socio-economic indicators were associated in the expected
direction with an increased risk of a time 2 episode (`persistent/recurrent'
cases). However, after adjustment for socio-demographic variables these
associations were reduced and became non-significant. Only participants
without educational qualifications showed an increased risk of a time 2
episode, but adjustment for baseline severity of symptoms further reduced the
association.

Odds ratios for an episode of common mental disorder at the 18-month
follow-up assessment in participants classified as cases at baseline
(n=750)1

The analysis of the separate ICD-10 diagnostic categories is shown in
Table 5. Generally the results
are similar to the combined analysis with the exception of the financial
difficulties variable. In depression, the reporting of financial difficulties
at baseline was significantly associated with an increased risk of a time 2
episode for both cohorts but stronger for cases at baseline
(persistent/recurrent cases).

Odds ratios for an episode of depression, anxiety disorder or non-specific
psychiatric morbidity by socio-economic position variables and baseline
disease status

DISCUSSION

We found little evidence that objective measures of socio-economic position
were associated with an episode of common mental disorder at follow-up, after
adjustment for confounding variables. From the indicators studied, we found
significant associations before adjusting for baseline psychiatric symptoms,
with a more subjective question on past financial difficulties for the cohort
of non-cases and lower educational qualifications for the cohort of cases.
These associations were reduced after adjustment. Separate analyses for
specific diagnoses showed that in depression, financial difficulties were
associated with an increased risk for both cohorts (but stronger for cases at
baseline), even after adjustment for baseline psychiatric symptoms. The latter
was the most consistent predictor of a time 2 episode for both cohorts.

Limitations of the study

Some limitations of the study should be considered. Participants were only
assessed at two time points 18 months apart and we do not have information
concerning their mental health for the period between the two assessments. In
addition, participants were not assessed for history of depression or anxiety
disorders at baseline. For those who were not categorised as cases at baseline
a new episode at follow-up could be either a first onset or a recurrence,
depending on their psychiatric history and their status in the period between
the two assessments. In addition, cases at baseline that were also cases at
follow-up could be either chronic persistent cases (not recovered) or
recurrences. It is also possible that some participants either developed or
recovered from an episode during the 18-month period and then reverted to
their original state by the end of the observation period. This imprecision
will certainly introduce measurement bias and possibly selection bias if the
duration of the episode is a confounding factor. These biases could influence
the results in either direction. An alternative method would be to ask
retrospectively about lifetime symptoms and symptoms during the 18-month
follow-up period. However, retrospective reporting of psychiatric symptoms has
been found to be unreliable and is also prone to recall bias
(Simon & Gureje, 1999).
There are examples from the Epidemiologic Catchment Area (ECA) study
suggesting that even the lifetime recall of psychiatric history is not very
reliable for depression (Thompson et
al, 2004) or anxiety disorders
(Nelson & Rice, 1997). It
should be noted that this limitation is also present in most of the previous
epidemiological studies concerning this issue. Gilman
(2003) noted in his commentary
on the meta-analysis by Lorant et al
(2003) that of the included
longitudinal studies only two out of five were `true' incident studies and
only one in four studies was designed specifically to assess `persistence' of
common mental disorders. In our own study, in order to avoid confusion, we
chose not to use the terms `onset' or `persistence', but rather to describe
exactly what we measured - that is, occurrence of a time 2 episode in the two
cohorts of non-cases and cases of common mental disorders at baseline.

Although the total sample size was large, our statistical power was still
limited and might have also contributed to our null findings, especially in
the analysis of the cohort of cases. Finally, loss to follow-up was greatest
among those in the lowest socio-economic groups, and although we used weights
to take into account non-response factors, our associations might have been
biased towards the null value.

Comparison with other studies

There are a few longitudinal studies with which this one may be compared.
The secondary analysis of the British Household Panel Survey
(Weich & Lewis,
1998b) was also conducted in the UK. That study found an
association between an index of poverty and persistence, but not episode
onset, at 12 months. It should be noted that the terms `persistence' and
`onset' as used in that study were completely analogous to the analyses of the
cohort of cases and non-cases presented here. That study also found that,
independently of case status, participants not managing well financially at
baseline (`financial strain') were more likely to report a new episode at
follow-up. In our study we did not find a significant association between our
index of material standard of living and an episode of common mental disorder
at follow-up, but our finding regarding financial difficulties is quite
similar. Reasons for this discrepancy in our findings may include the longer
interval of the follow-up (18 months) and the more detailed assessment of
common mental disorders, based on a structured clinical interview. The British
Household Panel Survey used the 12-item General Health Questionnaire, a
relatively simple self-reported instrument for the assessment of common mental
disorders (Weich & Lewis,
1998a). In the USA, longitudinal analyses of the Alameda
County study for onset of depression
(Kaplan et al, 1987)
and the ECA study for either onset (Bruce
et al, 1991; Horwath
et al, 1992; Bruce & Hoff, 1994; Eaton et
al, 2001) or persistence of common mental disorders
(Sargeant et al,
1990) are also of interest. Regarding onset, Horwath et
al (1992) and Eaton
et al (2001) using the
ECA data-set were unable to show a significant relationship between measures
of socio-economic status and onset of depression, after adjustment for
confounders. In contrast, in the Alameda County study the authors reported
significant associations for education, income and presence of `money
problems' at baseline (Kaplan et
al, 1987). An analysis from the New Haven ECA site
(Bruce et al, 1991)
did find an association between poverty and major depression after adjustment
for history of depression, but the results for other psychiatric disorders
were not significant, even though the point estimates for the odds ratios were
larger than 1.

When the analysis was restricted to first-onset depression
(Bruce & Hoff, 1994) the
authors reported a significant association between poverty and first-onset
major depression, but they presented odds ratios adjusted for age and gender
only. Regarding persistence of depression, Sargeant et al
(1990) using the ECA data-set
did not find any significant association with socio-economic status score,
lower education or persistence, after adjustment for baseline severity of
symptoms. Data from the Stirling County study in Canada showed that there was
a trend for low socio-economic status to be associated with both onset and
persistence of depression or anxiety, but these findings were not significant
after adjustment for age and gender
(Murphy et al, 1991).
A meta-analysis of longitudinal studies found a significant association
between socio-economic socio-economic indicators and both onset and
persistence, although the effect for persistence was larger
(Lorant et al, 2003).
However, it is worth noting that this meta-analysis was heavily influenced by
the results of the British Household Panel Survey
(Weich & Lewis,
1998b), which had the largest weight on both onset and
persistence. In addition, some of the papers included in the meta-analysis did
not adjust for potential confounders that made an important difference in our
own study. In our unadjusted analysis of the cohort of cases (the
`persistence' cohort) we found significant associations between all measures
of socio-economic position and a time 2 episode of common mental disorder, but
these disappeared when we adjusted for the other variables in the model.

In our main analysis we found significant associations with past financial
difficulties (in the cohort of non-cases) and lower education (in the cohort
of cases) only before adjustment for baseline CIS-R scores. However, it should
be noted that if baseline psychiatric morbidity is on the causal pathway
between low socio-economic position and onset or persistence of common mental
disorders, this could constitute an example of overadjustment. This is the
reason behind our choice of presenting the results before and after adjustment
for CIS-R scores.

The question on financial difficulties is more subjective in nature and
reflects the individuals' way of life. People in higher-income groups may, for
example, experience financial difficulties owing to overspending or
inappropriately raising their standard of living. In our main analysis there
was evidence that participants categorised as non-cases at baseline
experiencing financial difficulties had an increased risk of a time 2 episode,
even though the association became non-significant after adjustment for CIS-R
scores. Using depression as our dependent variable, the association was
significant in the full model and it was also observed in the cohort of cases.
These findings are consistent with research suggesting that subjective
measures of material standard of living may be equally important in the
relationship between socio-economic position and common mental disorders,
compared with the more objective measures of income or wealth
(Kaplan et al, 1987;
Lewis et al,
1998).

Our data also show that those in the economically inactive category had a
worse prognosis than those working full-time or part-time (see
Table 4). This category
included all those who reported that they were unable to work owing to
long-term illness or disability. Most of these people were deriving income
from state benefits (75% v. 11% of those working full- or part-time).
Separating these from the other economically inactive participants (students,
homemakers) increased further the association with persistence of common
mental disorders (OR 4.43, 95% CI 2.54-7.70). Participants with long-term
illness or disability were also more likely to report a new onset of disorder
(OR=2.56, 95% CI 1.10-5.94). An analogous finding was reported by the ECA
Baltimore follow-up study (Eaton et
al, 2001); in that analysis, although objective measures of
socio-economic position were not associated with onset of depression, a higher
psychological demand in the work environment and financial dependence on state
aid were found to be independently associated.

Baseline CIS-R scores were strongly associated with a time 2 episode for
both cohorts of cases and non-cases. This is consistent with previous research
(Sargeant et al,
1990; Horwath et al,
1992) and presumably reflects the chronic nature of many common
mental disorders. These findings emphasise the need to use methods for
prevention and treatment of common mental disorders similar to those used in
other chronic diseases such as diabetes or coronary heart disease
(Lloyd et al,
1996).

Is there a link between low socio-economic position and common mental
disorders? Pearlin et al
(1981) have argued that low
socio-economic status can be considered as an example of a chronic stressor
that increases the exposure to acute stressors and limits the psychosocial
resources for coping. Other possible mechanisms may include less perceived
social support (Wade & Kendler,
2000), lower control over one's environment
(Baum et al, 1993) and
unfavourable social comparison with others
(Ahrens & Alloy, 1997). In
addition to these indirect effects, low socio-economic position may have
direct effects on mental health. Link & Phelan
(2002) have proposed that low
socio-economic status can be viewed as a `fundamental cause' of disease, over
and above its effect on mediating mechanisms. What this and other studies add
is that the effects on mental health of objective measures of socio-economic
position, such as income or occupational social class, may have been
overestimated. Further research in more subjective measures of socio-economic
position is needed in order to improve our understanding of the mechanisms by
which socio-economic circumstances lead to depression and anxiety, if we are
to devise effective ways of preventing and treating these disorders.

Acknowledgments

The data collection was funded by the Department of Health and the Scottish
Executive Health Department. However, the views expressed in this report are
those of the authors alone and not necessarily those of the Department of
Health or Scottish Executive. We thank Dr Howard Meltzer for initial design
work on the survey and other Office for National Statistics staff who were
involved in the fieldwork and data preparation.

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